The use of PI and SigmaFine in the Water Industry

1. The use of PI and SigmaFine in the Water Industry B. D. Neve Rex

2. Introduction • In the last three decades water has increased dramatically in value and cost in both the clean water and waste utilities. • This is due to a number of factors, in particular environmental issues, population growth, urbanization, and in some areas climate change. • Water will never again be a free commodity, and indeed the cost is likely to go on rising at an even faster rate. • The Water and Waste utilities now realize that the implementation of industry standard control, SCADA, plant information and flow accounting are now fully justifiable and that data quality is vital to the stewardship of their valuable water assets. • The paper covers the real $ benefits of implementing PI and SigmaFine to cover flow balancing and accounting and data quality improvements which can be over 2 million $ per year for a 5 million household utility.

3. Overview • The PI implementation data in this paper comes from the experiences of Instem Beaver Valley, the distributor of PI in the UK • The SigmaFine data comes from the experiences of the author on 1 pilot and 2 commercial projects implementing SigmaFine in the Water industry to improve data quality. • In addition to work on the projects, the author conducted a study involving 6 large water companies in the UK on the $ benefits of improving data quality via data reconciliation

4. Users Custom centralised OMS system Custom communications to custom user interfaces Communications: Radio, PSTN, Satellite, Wide area networks, VPNs Scada systems Treatment plants Typical 80,90 and 2000 operational IT structure

5. Problems with the old Architecture • To much customised hardware and software • Supplier locks user into high costs and low performance compared to new solutions • Difficult and expensive to expand • knowledge of the system disappears which leads to misuse of data and degradation of data quality • Result is low data quality to the user • Bad business decisions, • E.g.money spent on wrong meters, costly manual studies to give robust water balances and on leakage detection

6. The Solution • Change OMS level to PI based standard systems • Install SigmaFine to audit, test improve data quality • Migrate SCADA to standard PC and Fieldbus based systems over time • This paper majors on improving data quality since such a project can have a significant hard ROI which can help pay for the other two enabling technologies

7. The Final Goal reconciled, accurate, consistent and auditable information stakeholders Overall reconciliation Distribution Input Flow balance reconciliation around storage and distribution Supply and plumbing losses Water taken legally unbilled Distribution losses Water taken illegally unbilled Flow balance around treatment plants Water delivered billed un-measured Water delivered billed measured Leakage monitoring and reduction systems

8. The use of PIin the Water Industry

9. Slide courtesy of Instem Beaver Valley PI In Use at Southern Water Telemetry Archive -------------------------- 100,000 PI points MTDB, PIQA & Tag Group Database WADIS & Report Manager PI-SCOPE Interface PI-CMS Interface Derived Values Archive --------------------------------- 5,000 PI points Derived Values Calculations Task Scheduler Exception Notification Servelec Regional Telemetry / SCADA SCOPE-X 4,000 Remote Sites

10. Slide courtesy of Instem Beaver Valley Telemetry Archive System Installed in late 1998 to replace P.ARCH and to provide: • Easy access to telemetry data • Larger SW audience • More accurate and complete data • Process data in a time frame meaningful to business

11. Slide courtesy of Instem Beaver Valley Telemetry Archive System Server 100,000 Point PI Data Archive Oracle PI Quality Archive PI SCOPE Interface PI CMS Interface Master Translation Database Tag Group Database Report Database Client PI-ProcessBook PI-DataLink PI-Manual Logger Archive Edit PIQA Viewer End-to-End Test Logging Report Manager Data View Tag Manager Tag Group manager

12. Slide courtesy of Instem Beaver Valley Derived Values Archive Installed in summer 2000 to provide: • Process Management & Water Resources Information System • Integration of data from a number of sources: • PI-UDS, Operational Database, WAACS, ISIS, QXP, … • Derivation of meaningful performance indicators of treatment processes (PM) • Maintenance of customer supplies using current hydrometric and antecedent conditions (WRIS)

13. Slide courtesy of Instem Beaver Valley DVA Calculations Telemetry PI Archive (PI-API) Microsoft Excel 25 Calculation Functions Multiple function calls Multiple sheets Exception Report DVA PI Archive PI-API Operational Database (ODBC) Remote Task Scheduler Manager Microsoft Task Scheduler

14. Slide courtesy of Instem Beaver Valley Thames Water PI System Server 10,000 Point PI Data Archive ABB Aqua Master Interface ABB Gateway Radcom Interface Radcom Gateway DMA Function Sets MeterMan Client PI-ProcessBook PI-DataLink Archive Edit PIQA Viewer Report Manager Data View Tag Manager Tag Group manager

15. PI in the water industry • PI is being used in the UK to supplement and replace the existing OMS systems • As existing OMS systems become more expensive and difficult to maintain more systems will transfer to cross industry standard systems such as PI • Meanwhile PI becomes an enabler for SigmaFine data reconciliation for improved data quality

16. PI in the water industry • Lessons Learnt: • PI copes well with the requirements for • Flexibility • Expandibility • PI is a cost effective purchase for water companies in terms of • Initial capital cost • Whole life cost

17. Improving Data Qualityin the Water Industry

18. The Problems that arise from inadequate data (1) • Unreliable leak detection and estimation • Water balancing: non closed balances, too much guess work, and lack of consistent history • Investment decisions based on inaccurate and inconsistent data • Difficulties in describing the networks

19. Problems that arise from inadequate data (2) • Little knowledge of meter accuracy, drift or bias • Different data in different parts of the company • Unknown operational/process performance • Arguments over shared asset agreements

20. The Problems that arise from inadequate data (3) • Unaccounted for flows • Difficult and resource consuming reporting to Water Regulator • Difficulties supporting arguments during billing disagreements • Difficulties in justifying increased monitoring or improved measurements

21. UK Regulator Ofwat reporting requirements • Section 2 Chapter 10 of July Return Reporting requirements& definitions manual • Water delivered forms the majority of the water balance. A company's approach to Table 10 can validate any assumptions used to estimate water delivered components. Ofwat encourages companies to estimate each component of distribution input and compare the sum of these with measured distribution input. Where there is a small discrepancy (say less than I or 2%) this can be allocated to those components with the greatest uncertainty. A large discrepancy suggests that a review of a company's estimating process is required, as it is clearly not satisfactory for a company to be unable to account fully for its major product. • The company should give an explicit explanation of any reconciliation adjustment, indicating which water balance components have received the adjustment using the Maximum Likelihood Estimation method. Where the company's estimating process has been reviewed the company should provide a full briefing; outlining the degree of the discrepancy, which components were reviewed, what assumptions were altered, and is so why, and which water balance components needed improvement.

22. Ofwat reporting requirements • To estimate distribution losses (Mld) companies should use the Integrated Flow Method. The resultant leakage level should then be checked against monitored night flows. Companies should therefore use the Integrated Flow Method and the Minimum Night Flow Method in conjunction, as a means to substantiate their estimation of leakage. • Ofwat would also encourage companies to support estimates with effective data monitoring systems; an example would be a domestic consumption monitor used by Severn Trent Water to support their estimate of unmeasured household per capita consumption. • Ofwat would also expect to see the impact of metering on some water delivered components:

23. Ofwat reporting requirements • Distribution input (Mld • Reliability Grade A The sum of the separately estimated water balance components reconcile with the measured volume of distribution input to within 1-2%. There has been no adjustment made to measured distribution input other than as a result of the aforementioned reconciliation; that is, the sum of the water balance components with measured distribution input. Measured distribution input has been estimated from water-into-supply meters which record 95% of the volume of distribution input, and the meters have been used and regularly recalibrated in accordance with the manufacturers recommendations. • Reliability Grade B The sum of the separately estimated water balance components reconcile with the measured volume of distribution input to within 5% but not to within 2%. There has been no adjustment made to measured distribution input, other than as a result of the aforementioned reconciliation; that is, the sum of the water balance components with measured distribution input. Measured distribution input has been estimated from water-into-supply meters which record 90% of the volume of distribution input, and the meters have been used and regularly recalibrated in accordance with the manufacturers recommendations.

24. Ofwat reporting requirements • Overall water balance • Reliability Grade A The water balance components reconcile with measured distribution input to within 2%. An explicit explanation for any reconciliation adjustment is given and an adjustment has been made to distribution input or has been distributed between water balance components. Water-into-supply meters have been used and recalibrated in accordance with the manufacturers recommendation. The water balance components have been separately estimated and reconcile with the equivalent residual of the water balance. 90% of the volume of distribution input (not including distribution input) has been awarded a reliability band of A or B within the separately estimated water balance components. • Reliability Grade B The water balance components do not reconcile with measured distribution input to within 5%, hence an adjustment has been made to distribution input or has been distributed between water balance components using the Maximum Likelihood Estimation technique. Water-into-supply meters have been used and recalibrated in accordance with the manufacturers recommendation. The water balance components have been separately estimated and reconcile with the equivalent residual of the water balance. 90% of the volume of distribution input should have been awarded a reliability band of A or B within the separately estimated water balance components.

25. Sources of data quality problems • Measurement/metering errors • Plant/Network errors • Hidden flows or leaks • Un-metered flows • Un-measured inventory changes • Dynamic effects • Data processing errors

26. Sources of Flow MeasurementError • Installation Effects • Precision • Fouling • Fossilized Bias (buttered toast)

27. Measurement Uncertainty Daily performance

28. Data Processing Errors • Manual data entry systems • Multiple values for single data points • Incorrect engineering calculations • Lack of time synchronization measurements • Different end of period for accounting and engineering • Data is historized and stored in multiple locations • Data is changed and “fixed” by multiple functional areas • Supply, Distribution, Planning, Engineering Accounting • Control

29. How to improve the data • Carry out a top down data quality improvement project • Use “Data Reconciliation” as an integral element • A proven method from the Oil and Petrochemical industries

30. Conventional Wisdom on Data Quality • Engineering and accounting data are different • Meter errors balance out in the long run • Volume balances are the same as mass balances • Mass balances are simple • Mass balances are impossible • Manual estimates are not important • Accounting data does not matter • Custody transfer measurements are correct • Inventory measurements have no variance

31. Measures of Data Quality • Completeness • Meters, inventories, Transactions, Composition, Densities • Redundancy • How many times is the same volume measured • Precision • What is the variance of the measurement device • Accuracy • How is the measurement compared to a standard

32. Data Quality Analytical Tools • Expert systems • Neural networks • Reconciliation systems

33. Expert Systems • Rules of Thumb • Complex to build and maintain relationships • Useful for gross error detection

34. Neural Networks • Recognizes patterns • Model setup is important • Accurate to a few percent • Useful for gross error detection

35. Data reconciliation • Data reconciliation is a systematic way of using allthe available information about a process or system or business to improve consistency and accuracy • Very often some information is overlooked • This information can be flows, inventories, levels, , meter accuracies, loss estimates and equations i.e.. mass balances, component balances, energy balances • Sigmafine is an advanced data reconciliation package designed for the process and utility industries

36. The theory behind data reconciliation Reconciled value i.e.. best estimate of value consistent with all information 4.5 ML/day Flow Meter Level Meter Integrated flow reading Change in inventory Delta Level Average Area Tolerance of flow meter Tolerance of level measurement Tolerance of reconciled value 0.0 ML/day

37. The mathematics • The SigmaFine Data reconciliation algorithm distributes all the errors in proportion to the confidences on the data (e.g. meter readings) so that: • All the balances are precisely satisfied • the total sum of the perturbations on the data is minimised • The sum is the squared deviation normalised by the confidence on each piece of the data. • This is a large constrained minimum sum of errors squared problem and uses a Kalman filtering algorithm.

38. The history of data reconciliation • Data reconciliation has been used for 20 years in the Oil and Petrochemical sector • It produces accurate material, energy and component balances • It helps the accountants track expensive feed, intermediates and products and account for losses • Before SigmaFine, data reconciliation was expensive and cumbersome to use

39. Invensys Honeywell THE FIX IN TOUCH SCADA What is the SigmaFine package NETWORK MODELS AND CASES RECONCILEDDATA OTHER PROGRAMS APPLICATIONS SQL AD HOC REPORTS PI DATA RECONCILIATION SIGMAFINE The optimised reconciliation algorithm HISTORICAL Process Book REPORTS • WATER BALANCES • LEAK ESTIMATES • INVENTORIES • ETC. PUMPS OTHER INPUTS RESERVOIRS FLOW METERS MANUAL DATA NIGHT LINE DATA

40. How SigmaFine deals with inventories For irregularly shaped tanks or reservoirs, SigmaFine has an automatic built in “strapping” feature The vessel is divided into a number of slices and each slice has an area associated with it. The program interpolates linearly between the slices to calculate the area at any depth and thus the change in volume for any change in depth.

41. Typical applications • Accounting mass/water balance • Operational water balance • Leakage estimation and tracking • Suspect meter reports/meter proving • Dosing component balance • Dosing accuracy improvement • Shared processing/asset agreements

42. Applications continued • Preparation of data to Regulator • Water stock monitoring/reporting • Recovery support after upset • Network description and documentation • Training of operational personnel • Historical performance reporting

43. Applications continued • Mass, volume and component balances at treatment plants • Mass and volume balances around sewage works • Improved process knowledge • Diurnal flow estimation and balancing • Adverse trend detection, e.g. solids build-up

44. Applications continued • Identification of problems • Instrument/meter problems • Badly installed, faulty, or biased meters • Faulty calibration or instrument drift • Missing measurements • Model or network knowledge errors • unaccounted or missing flows • incorrect association of data • incorrect time stamping

45. Implementation • Attend training course (3 days) Install on target desktop computer • Develop initial model/network (few days to few months depending on size) • Set up auto transfer of data PI, and accounting systems • Debug model, test data • Develop and enlarge model in line with business needs. Migrate to larger machine or network.

46. SIGMAfine MonthlyOperations Overall reconciliation Distribution Input Flow balance reconciliation around storage and distribution Supply and plumbing losses Water taken legally unbilled Distribution losses Water taken illegally unbilled Water delivered billed un-measured Flow balance around treatment plants Water delivered billed measured

47. Using Sigmafine balance area balance area balance area balance area balance area

48. Zone 1 Zone 3 Zone 2 Zone 4 Zone 5 Zone 6 balance area balance area balance area balance area balance area • SigmaFine lets you describe a network of treatment works, pipes, pumps, reservoirs and meters as a live intelligent graphic which you can change at any time • The reconciled balance formula are derived automatically from this picture when you run a balance

49. SigmaFine lets you build up your balances from small local zones through district to division and company wide balances or vice versa • Everyone can access a standard updated network and can alter their own local copy for test runs, feasibility studies, investment decisions etc District balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area balance area

50. Typical SigmaFine Screen